
// g++-4.4 bench_gemm.cpp -I .. -O2 -DNDEBUG -lrt -fopenmp && OMP_NUM_THREADS=2  ./a.out
// icpc bench_gemm.cpp -I .. -O3 -DNDEBUG -lrt -openmp  && OMP_NUM_THREADS=2  ./a.out

// Compilation options:
//
// -DSCALAR=std::complex<double>
// -DSCALARA=double or -DSCALARB=double
// -DHAVE_BLAS
// -DDECOUPLED
//

#include <Eigen/Core>
#include <bench/BenchTimer.h>
#include <iostream>

using namespace std;
using namespace Eigen;

#ifndef SCALAR
// #define SCALAR std::complex<float>
#define SCALAR float
#endif

#ifndef SCALARA
#define SCALARA SCALAR
#endif

#ifndef SCALARB
#define SCALARB SCALAR
#endif

#ifdef ROWMAJ_A
const int opt_A = RowMajor;
#else
const int opt_A = ColMajor;
#endif

#ifdef ROWMAJ_B
const int opt_B = RowMajor;
#else
const int opt_B = ColMajor;
#endif

typedef SCALAR Scalar;
typedef NumTraits<Scalar>::Real RealScalar;
typedef Matrix<SCALARA, Dynamic, Dynamic, opt_A> A;
typedef Matrix<SCALARB, Dynamic, Dynamic, opt_B> B;
typedef Matrix<Scalar, Dynamic, Dynamic> C;
typedef Matrix<RealScalar, Dynamic, Dynamic> M;

#ifdef HAVE_BLAS

extern "C"
{
#include <Eigen/src/misc/blas.h>
}

static float fone = 1;
static float fzero = 0;
static double done = 1;
static double szero = 0;
static std::complex<float> cfone = 1;
static std::complex<float> cfzero = 0;
static std::complex<double> cdone = 1;
static std::complex<double> cdzero = 0;
static char notrans = 'N';
static char trans = 'T';
static char nonunit = 'N';
static char lower = 'L';
static char right = 'R';
static int intone = 1;

#ifdef ROWMAJ_A
const char transA = trans;
#else
const char transA = notrans;
#endif

#ifdef ROWMAJ_B
const char transB = trans;
#else
const char transB = notrans;
#endif

template<typename A, typename B>
void
blas_gemm(const A& a, const B& b, MatrixXf& c)
{
	int M = c.rows();
	int N = c.cols();
	int K = a.cols();
	int lda = a.outerStride();
	int ldb = b.outerStride();
	int ldc = c.rows();

	sgemm_(&transA,
		   &transB,
		   &M,
		   &N,
		   &K,
		   &fone,
		   const_cast<float*>(a.data()),
		   &lda,
		   const_cast<float*>(b.data()),
		   &ldb,
		   &fone,
		   c.data(),
		   &ldc);
}

template<typename A, typename B>
void
blas_gemm(const A& a, const B& b, MatrixXd& c)
{
	int M = c.rows();
	int N = c.cols();
	int K = a.cols();
	int lda = a.outerStride();
	int ldb = b.outerStride();
	int ldc = c.rows();

	dgemm_(&transA,
		   &transB,
		   &M,
		   &N,
		   &K,
		   &done,
		   const_cast<double*>(a.data()),
		   &lda,
		   const_cast<double*>(b.data()),
		   &ldb,
		   &done,
		   c.data(),
		   &ldc);
}

template<typename A, typename B>
void
blas_gemm(const A& a, const B& b, MatrixXcf& c)
{
	int M = c.rows();
	int N = c.cols();
	int K = a.cols();
	int lda = a.outerStride();
	int ldb = b.outerStride();
	int ldc = c.rows();

	cgemm_(&transA,
		   &transB,
		   &M,
		   &N,
		   &K,
		   (float*)&cfone,
		   const_cast<float*>((const float*)a.data()),
		   &lda,
		   const_cast<float*>((const float*)b.data()),
		   &ldb,
		   (float*)&cfone,
		   (float*)c.data(),
		   &ldc);
}

template<typename A, typename B>
void
blas_gemm(const A& a, const B& b, MatrixXcd& c)
{
	int M = c.rows();
	int N = c.cols();
	int K = a.cols();
	int lda = a.outerStride();
	int ldb = b.outerStride();
	int ldc = c.rows();

	zgemm_(&transA,
		   &transB,
		   &M,
		   &N,
		   &K,
		   (double*)&cdone,
		   const_cast<double*>((const double*)a.data()),
		   &lda,
		   const_cast<double*>((const double*)b.data()),
		   &ldb,
		   (double*)&cdone,
		   (double*)c.data(),
		   &ldc);
}

#endif

void
matlab_cplx_cplx(const M& ar, const M& ai, const M& br, const M& bi, M& cr, M& ci)
{
	cr.noalias() += ar * br;
	cr.noalias() -= ai * bi;
	ci.noalias() += ar * bi;
	ci.noalias() += ai * br;
	// [cr ci] += [ar ai] * br + [-ai ar] * bi
}

void
matlab_real_cplx(const M& a, const M& br, const M& bi, M& cr, M& ci)
{
	cr.noalias() += a * br;
	ci.noalias() += a * bi;
}

void
matlab_cplx_real(const M& ar, const M& ai, const M& b, M& cr, M& ci)
{
	cr.noalias() += ar * b;
	ci.noalias() += ai * b;
}

template<typename A, typename B, typename C>
EIGEN_DONT_INLINE void
gemm(const A& a, const B& b, C& c)
{
	c.noalias() += a * b;
}

int
main(int argc, char** argv)
{
	std::ptrdiff_t l1 = internal::queryL1CacheSize();
	std::ptrdiff_t l2 = internal::queryTopLevelCacheSize();
	std::cout << "L1 cache size     = " << (l1 > 0 ? l1 / 1024 : -1) << " KB\n";
	std::cout << "L2/L3 cache size  = " << (l2 > 0 ? l2 / 1024 : -1) << " KB\n";
	typedef internal::gebp_traits<Scalar, Scalar> Traits;
	std::cout << "Register blocking = " << Traits::mr << " x " << Traits::nr << "\n";

	int rep = 1;   // number of repetitions per try
	int tries = 2; // number of tries, we keep the best

	int s = 2048;
	int m = s;
	int n = s;
	int p = s;
	int cache_size1 = -1, cache_size2 = l2, cache_size3 = 0;

	bool need_help = false;
	for (int i = 1; i < argc;) {
		if (argv[i][0] == '-') {
			if (argv[i][1] == 's') {
				++i;
				s = atoi(argv[i++]);
				m = n = p = s;
				if (argv[i][0] != '-') {
					n = atoi(argv[i++]);
					p = atoi(argv[i++]);
				}
			} else if (argv[i][1] == 'c') {
				++i;
				cache_size1 = atoi(argv[i++]);
				if (argv[i][0] != '-') {
					cache_size2 = atoi(argv[i++]);
					if (argv[i][0] != '-')
						cache_size3 = atoi(argv[i++]);
				}
			} else if (argv[i][1] == 't') {
				tries = atoi(argv[++i]);
				++i;
			} else if (argv[i][1] == 'p') {
				++i;
				rep = atoi(argv[i++]);
			}
		} else {
			need_help = true;
			break;
		}
	}

	if (need_help) {
		std::cout << argv[0] << " -s <matrix sizes> -c <cache sizes> -t <nb tries> -p <nb repeats>\n";
		std::cout << "   <matrix sizes> : size\n";
		std::cout << "   <matrix sizes> : rows columns depth\n";
		return 1;
	}

#if EIGEN_VERSION_AT_LEAST(3, 2, 90)
	if (cache_size1 > 0)
		setCpuCacheSizes(cache_size1, cache_size2, cache_size3);
#endif

	A a(m, p);
	a.setRandom();
	B b(p, n);
	b.setRandom();
	C c(m, n);
	c.setOnes();
	C rc = c;

	std::cout << "Matrix sizes = " << m << "x" << p << " * " << p << "x" << n << "\n";
	std::ptrdiff_t mc(m), nc(n), kc(p);
	internal::computeProductBlockingSizes<Scalar, Scalar>(kc, mc, nc);
	std::cout << "blocking size (mc x kc) = " << mc << " x " << kc << " x " << nc << "\n";

	C r = c;

// check the parallel product is correct
#if defined EIGEN_HAS_OPENMP
	Eigen::initParallel();
	int procs = omp_get_max_threads();
	if (procs > 1) {
#ifdef HAVE_BLAS
		blas_gemm(a, b, r);
#else
		omp_set_num_threads(1);
		r.noalias() += a * b;
		omp_set_num_threads(procs);
#endif
		c.noalias() += a * b;
		if (!r.isApprox(c))
			std::cerr << "Warning, your parallel product is crap!\n\n";
	}
#elif defined HAVE_BLAS
	blas_gemm(a, b, r);
	c.noalias() += a * b;
	if (!r.isApprox(c)) {
		std::cout << (r - c).norm() / r.norm() << "\n";
		std::cerr << "Warning, your product is crap!\n\n";
	}
#else
	if (1. * m * n * p < 2000. * 2000 * 2000) {
		gemm(a, b, c);
		r.noalias() += a.cast<Scalar>().lazyProduct(b.cast<Scalar>());
		if (!r.isApprox(c)) {
			std::cout << (r - c).norm() / r.norm() << "\n";
			std::cerr << "Warning, your product is crap!\n\n";
		}
	}
#endif

#ifdef HAVE_BLAS
	BenchTimer tblas;
	c = rc;
	BENCH(tblas, tries, rep, blas_gemm(a, b, c));
	std::cout << "blas  cpu         " << tblas.best(CPU_TIMER) / rep << "s  \t"
			  << (double(m) * n * p * rep * 2 / tblas.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << tblas.total(CPU_TIMER)
			  << "s)\n";
	std::cout << "blas  real        " << tblas.best(REAL_TIMER) / rep << "s  \t"
			  << (double(m) * n * p * rep * 2 / tblas.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t("
			  << tblas.total(REAL_TIMER) << "s)\n";
#endif

	// warm start
	if (b.norm() + a.norm() == 123.554)
		std::cout << "\n";

	BenchTimer tmt;
	c = rc;
	BENCH(tmt, tries, rep, gemm(a, b, c));
	std::cout << "eigen cpu         " << tmt.best(CPU_TIMER) / rep << "s  \t"
			  << (double(m) * n * p * rep * 2 / tmt.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << tmt.total(CPU_TIMER)
			  << "s)\n";
	std::cout << "eigen real        " << tmt.best(REAL_TIMER) / rep << "s  \t"
			  << (double(m) * n * p * rep * 2 / tmt.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << tmt.total(REAL_TIMER)
			  << "s)\n";

#ifdef EIGEN_HAS_OPENMP
	if (procs > 1) {
		BenchTimer tmono;
		omp_set_num_threads(1);
		Eigen::setNbThreads(1);
		c = rc;
		BENCH(tmono, tries, rep, gemm(a, b, c));
		std::cout << "eigen mono cpu    " << tmono.best(CPU_TIMER) / rep << "s  \t"
				  << (double(m) * n * p * rep * 2 / tmono.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t("
				  << tmono.total(CPU_TIMER) << "s)\n";
		std::cout << "eigen mono real   " << tmono.best(REAL_TIMER) / rep << "s  \t"
				  << (double(m) * n * p * rep * 2 / tmono.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t("
				  << tmono.total(REAL_TIMER) << "s)\n";
		std::cout << "mt speed up x" << tmono.best(CPU_TIMER) / tmt.best(REAL_TIMER) << " => "
				  << (100.0 * tmono.best(CPU_TIMER) / tmt.best(REAL_TIMER)) / procs << "%\n";
	}
#endif

	if (1. * m * n * p < 30 * 30 * 30) {
		BenchTimer tmt;
		c = rc;
		BENCH(tmt, tries, rep, c.noalias() += a.lazyProduct(b));
		std::cout << "lazy cpu         " << tmt.best(CPU_TIMER) / rep << "s  \t"
				  << (double(m) * n * p * rep * 2 / tmt.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << tmt.total(CPU_TIMER)
				  << "s)\n";
		std::cout << "lazy real        " << tmt.best(REAL_TIMER) / rep << "s  \t"
				  << (double(m) * n * p * rep * 2 / tmt.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t("
				  << tmt.total(REAL_TIMER) << "s)\n";
	}

#ifdef DECOUPLED
	if ((NumTraits<A::Scalar>::IsComplex) && (NumTraits<B::Scalar>::IsComplex)) {
		M ar(m, p);
		ar.setRandom();
		M ai(m, p);
		ai.setRandom();
		M br(p, n);
		br.setRandom();
		M bi(p, n);
		bi.setRandom();
		M cr(m, n);
		cr.setRandom();
		M ci(m, n);
		ci.setRandom();

		BenchTimer t;
		BENCH(t, tries, rep, matlab_cplx_cplx(ar, ai, br, bi, cr, ci));
		std::cout << "\"matlab\" cpu    " << t.best(CPU_TIMER) / rep << "s  \t"
				  << (double(m) * n * p * rep * 2 / t.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(CPU_TIMER)
				  << "s)\n";
		std::cout << "\"matlab\" real   " << t.best(REAL_TIMER) / rep << "s  \t"
				  << (double(m) * n * p * rep * 2 / t.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(REAL_TIMER)
				  << "s)\n";
	}
	if ((!NumTraits<A::Scalar>::IsComplex) && (NumTraits<B::Scalar>::IsComplex)) {
		M a(m, p);
		a.setRandom();
		M br(p, n);
		br.setRandom();
		M bi(p, n);
		bi.setRandom();
		M cr(m, n);
		cr.setRandom();
		M ci(m, n);
		ci.setRandom();

		BenchTimer t;
		BENCH(t, tries, rep, matlab_real_cplx(a, br, bi, cr, ci));
		std::cout << "\"matlab\" cpu    " << t.best(CPU_TIMER) / rep << "s  \t"
				  << (double(m) * n * p * rep * 2 / t.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(CPU_TIMER)
				  << "s)\n";
		std::cout << "\"matlab\" real   " << t.best(REAL_TIMER) / rep << "s  \t"
				  << (double(m) * n * p * rep * 2 / t.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(REAL_TIMER)
				  << "s)\n";
	}
	if ((NumTraits<A::Scalar>::IsComplex) && (!NumTraits<B::Scalar>::IsComplex)) {
		M ar(m, p);
		ar.setRandom();
		M ai(m, p);
		ai.setRandom();
		M b(p, n);
		b.setRandom();
		M cr(m, n);
		cr.setRandom();
		M ci(m, n);
		ci.setRandom();

		BenchTimer t;
		BENCH(t, tries, rep, matlab_cplx_real(ar, ai, b, cr, ci));
		std::cout << "\"matlab\" cpu    " << t.best(CPU_TIMER) / rep << "s  \t"
				  << (double(m) * n * p * rep * 2 / t.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(CPU_TIMER)
				  << "s)\n";
		std::cout << "\"matlab\" real   " << t.best(REAL_TIMER) / rep << "s  \t"
				  << (double(m) * n * p * rep * 2 / t.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(REAL_TIMER)
				  << "s)\n";
	}
#endif

	return 0;
}
